Embedded Machine Learning - Mdhaffar, Afef
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  • Broschiertes Buch

This book represents a first step towards embedded machine learning. It presents techniques for optimizing and compressing deep learning models. These techniques make it easier to deploy a high-performance, lightweight deep learning model on resource-constrained devices such as smartphones and microcontrollers. This paper also explores a topical knowledge transfer technique, namely knowledge distillation. This technique makes it possible to improve the performance of a lightweight deep learning model, while transferring to it the knowledge of a complex, high-performance deep learning model.…mehr

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Produktbeschreibung
This book represents a first step towards embedded machine learning. It presents techniques for optimizing and compressing deep learning models. These techniques make it easier to deploy a high-performance, lightweight deep learning model on resource-constrained devices such as smartphones and microcontrollers. This paper also explores a topical knowledge transfer technique, namely knowledge distillation. This technique makes it possible to improve the performance of a lightweight deep learning model, while transferring to it the knowledge of a complex, high-performance deep learning model. All these techniques have been detailed in this book and illustrated with practical Python implementations, generally based on the use of the pytorch and tensorflow libraries.
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Autorenporträt
Afef Mdhaffar received her PhD in Computer Science in 2014 from the University of Marburg, Germany. She is currently a lecturer in computer science at the École Nationale d'Ingénieurs de Sfax (ENIS), Tunisia. Her current work focuses on deep learning model optimization techniques.